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Volume 15, Number 4—April 2009

Research

Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

Jerome I. TokarsComments to Author , Howard Burkom, Jian Xing, Roseanne English, Steven Bloom, Kenneth Cox, and Julie A. Pavlin
Author affiliations: Centers for Disease Control and Prevention, Atlanta, Georgia, USA (J.I. Tokars, J. Xing, R. English); The Johns Hopkins University, Baltimore, Maryland, USA (H. Burkom); Science Applications Incorporated, San Diego, California, USA (S. Bloom); Department of Defense, Washington, DC, USA (K. Cox); Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand (J.A. Pavlin)

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Figure 2

Sensitivity of detecting various numbers of additional counts, by using initial versus best algorithms for hospital emergency department chief complaint data, for selected BioSense data. Red line shows the initial algorithm (minimum SD = 0.2, 7-day baseline, count method, unstratified baseline), and black line shows the best algorithm (minimum SD = 1.0, 28-day baseline, rate method, unstratified baseline).

Figure 2. Sensitivity of detecting various numbers of additional counts, by using initial versus best algorithms for hospital emergency department chief complaint data, for selected BioSense data. Red line shows the initial algorithm (minimum SD = 0.2, 7-day baseline, count method, unstratified baseline), and black line shows the best algorithm (minimum SD = 1.0, 28-day baseline, rate method, unstratified baseline).

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